Abstract Although cigarette smoking is the major risk factor, multiple studies have demonstrated a genetic component to chronic obstructive pulmonary disease (COPD) susceptibility. Genome Wide Association Studies (GWAS) have identified Single Nucleotide Polymorphisms (SNPs) that have a significant association with COPD. Beyond the informative top findings, many GWAS results are below the genome-wide threshold for significance and are typically ignored. The central hypothesis of this study is that sub-threshold GWAS SNPs confer susceptibility to COPD. We will develop a publicly available ensemble analysis tool to elucidate susceptibility factors from prior GWAS using genomic, epigenomic and genetic data in lung tissue. This integrative omics method will aggregate the gene expression effects from all potentially relevant SNPs by extracting the additional genetic and genomic signals contained in the sub-threshold results, from the biological and technical noise. The electrical engineering background of the applicant provides a valuable perspective for the extraction of the signals from noise, as well as experience in the creation of solutions from custom-tailored building blocks. His M.S. degrees in Biotechnology and Bioinformatics provide a foundation for biomedical research that has been centered on COPD, in particular the analysis of multidimensional omics and phenotype data. Training and mentorship within the Channing Division of Network Medicine (CDNM) at Brigham and Women?s Hospital will impart the necessary knowledge in lung disease biology, statistics, network methods and software development to succeed in this study and move toward independence. The CDNM has a well-established research program in respiratory, environmental and genetic epidemiology, pharmacogenetics and genomics, statistical genetics, bioinformatics, epigenetics and network medicine. This environment provides access to multiple didactic activities, within the Division and through neighboring institutes, such as seminars and lectures that will facilitate the applicant's training. The applicant will attain new skills to develop new methods and adapt existing ones to serve as components in the proposed pipeline. In this study, we will aggregate the effects of sub-threshold GWAS to implicate genes in the etiology of disease using the Bayesian method Sherlock, and adapt Sherlock for use with DNA methylation data. We will construct networks using omics data, and develop methods to observe edge perturbations associated with the genotypes of sub-threshold GWAS SNPs to highlight their regulatory influence. We will create between-network links based on causal evidence and identify gene expression regions influenced by epigenetic mediation. The network-based evidence from these aims will highlight gene communities affecting COPD susceptibility, which may inform the development of future personalized therapies. Through the proposed career development and mentored research activities, the applicant will progress towards the goal of becoming an independent bioinformatics scientist, with computational and statistics expertise, trained in the study of complex lung disease.